Hi again! This is another great topic. We don't use an A/B test platform, but we leverage native A/B testing capabilities in both Chameleon and Customer.io. Here's how we'd consider approaching Optimizely:

Data Integration

• Capture Optimizely events (e.g., experiment exposures, variant selections, conversions) via a webhook or Segment integration.
• Map these events to user profiles using userId, email, or anonymousId.
• Store the data in Segment for activation.

Key Traits to Derive

• Experiment Exposure – Tracks which experiments a user has seen and their assigned variant.
• Conversion Likelihood – Based on past experiment interactions, estimate a user’s likelihood to convert.
• Feature Adoption Score – Measures how often a user engages with new features released via Optimizely.
• Optimal UX Path – Identifies which test variant leads to higher engagement/conversions for a user.
• Test Fatigue – Flags users who’ve been exposed to too many experiments to avoid over-testing.

Use Cases

• Personalized Campaigns – Adjust messaging/timing based on a user’s past experiment exposure.
• Targeted Rollouts – Identify users who responded well to a variant and push them into an early feature rollout. (Re-targeting customers based on intent converts REALLY well for us!)
• Retention Insights – Correlate experiment engagement with long-term retention and LTV. Apply those validated learnings to new users.

And of course some recipes we love starring Optimizely:
Website personalization with Google search keywords
Personalized landing pages
A/B testing your marketing stack